The tensor pooling module highlights the baggage content transitions in the image gradients oriented in \(N\)  directions and up to \(n\)  scaling levels.
Increasing these parameters helps generate the best contour representation leading towards a more robust detection, but also incurs additional computational cost. As depicted in Figure REF  (A), we can see that for GDXray dataset [1]} with \(N=2\) , \(n=2\) , we obtain an mAP score of 0.82. With the combination \(N=5\) , \(n=5\) , we get 16.54% improvements in the detection performance but at the expense of a 97.71% increase in computational time (see Figure REF -D). Similarly, on the SIXray dataset [2]}, we obtain 18.36% improvements in the detection performance (by increasing \(N\)  and \(n\) ) at the expense of 95.88% in the computational time (see Figure REF  (B, E)). The same behavior is also noticed for OPIXray dataset [3]} in Figure REF  (C, F). Considering all the combinations depicted in Figure REF , we found that \(N=4\)  and \(n=3\)  provide the best trade-off between the detection and run-time performance across all three datasets.
